
// g++ -O3 -g0 -DNDEBUG  sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
// g++ -O3 -g0 -DNDEBUG  sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
//  -DNOGMM -DNOMTL -DCSPARSE
//  -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
#ifndef SIZE
#    define SIZE 650000
#endif

#ifndef DENSITY
#    define DENSITY 0.01
#endif

#ifndef REPEAT
#    define REPEAT 1
#endif

#include "BenchSparseUtil.h"

#ifndef MINDENSITY
#    define MINDENSITY 0.0004
#endif

#ifndef NBTRIES
#    define NBTRIES 10
#endif

#define BENCH(X)                                \
    timer.reset();                              \
    for ( int _j = 0; _j < NBTRIES; ++_j ) {    \
        timer.start();                          \
        for ( int _k = 0; _k < REPEAT; ++_k ) { \
            X                                   \
        }                                       \
        timer.stop();                           \
    }


#ifdef CSPARSE
cs* cs_sorted_multiply(const cs* a, const cs* b)
{
    cs* A = cs_transpose(a, 1);
    cs* B = cs_transpose(b, 1);
    cs* D = cs_multiply(B, A); /* D = B'*A' */
    cs_spfree(A);
    cs_spfree(B);
    cs_dropzeros(D);            /* drop zeros from D */
    cs* C = cs_transpose(D, 1); /* C = D', so that C is sorted */
    cs_spfree(D);
    return C;
}
#endif

int main(int argc, char* argv[])
{
    int   rows    = SIZE;
    int   cols    = SIZE;
    float density = DENSITY;

    EigenSparseMatrix sm1(rows, cols);
    DenseVector       v1(cols), v2(cols);
    v1.setRandom();

    BenchTimer timer;
    for ( float density = DENSITY; density >= MINDENSITY; density *= 0.5 ) {
        // fillMatrix(density, rows, cols, sm1);
        fillMatrix2(7, rows, cols, sm1);

// dense matrices
#ifdef DENSEMATRIX
        {
            std::cout << "Eigen Dense\t" << density * 100 << "%\n";
            DenseMatrix m1(rows, cols);
            eiToDense(sm1, m1);

            timer.reset();
            timer.start();
            for ( int k = 0; k < REPEAT; ++k )
                v2 = m1 * v1;
            timer.stop();
            std::cout << "   a * v:\t" << timer.best() << "  " << double(REPEAT) / timer.best() << " * / sec " << endl;

            timer.reset();
            timer.start();
            for ( int k = 0; k < REPEAT; ++k )
                v2 = m1.transpose() * v1;
            timer.stop();
            std::cout << "   a' * v:\t" << timer.best() << endl;
        }
#endif

        // eigen3 sparse matrices
        {
            std::cout << "Eigen sparse\t" << sm1.nonZeros() / float(sm1.rows() * sm1.cols()) * 100 << "%\n";

            BENCH(asm("#myc"); v2 = sm1 * v1; asm("#myd");)
            std::cout << "   a * v:\t" << timer.best() / REPEAT << "  " << double(REPEAT) / timer.best(REAL_TIMER) << " * / sec " << endl;


            BENCH({ asm("#mya"); v2 = sm1.transpose() * v1; asm("#myb"); })

            std::cout << "   a' * v:\t" << timer.best() / REPEAT << endl;
        }

        //     {
        //       DynamicSparseMatrix<Scalar> m1(sm1);
        //       std::cout << "Eigen dyn-sparse\t" << m1.nonZeros()/float(m1.rows()*m1.cols())*100 << "%\n";
        //
        //       BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1 * v1;)
        //       std::cout << "   a * v:\t" << timer.value() << endl;
        //
        //       BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1.transpose() * v1;)
        //       std::cout << "   a' * v:\t" << timer.value() << endl;
        //     }

// GMM++
#ifndef NOGMM
        {
            std::cout << "GMM++ sparse\t" << density * 100 << "%\n";
            // GmmDynSparse  gmmT3(rows,cols);
            GmmSparse m1(rows, cols);
            eiToGmm(sm1, m1);

            std::vector<Scalar> gmmV1(cols), gmmV2(cols);
            Map<Matrix<Scalar, Dynamic, 1>>(&gmmV1[0], cols) = v1;
            Map<Matrix<Scalar, Dynamic, 1>>(&gmmV2[0], cols) = v2;

            BENCH(asm("#myx"); gmm::mult(m1, gmmV1, gmmV2); asm("#myy");)
            std::cout << "   a * v:\t" << timer.value() << endl;

            BENCH(gmm::mult(gmm::transposed(m1), gmmV1, gmmV2);)
            std::cout << "   a' * v:\t" << timer.value() << endl;
        }
#endif

#ifndef NOUBLAS
        {
            std::cout << "ublas sparse\t" << density * 100 << "%\n";
            UBlasSparse m1(rows, cols);
            eiToUblas(sm1, m1);

            boost::numeric::ublas::vector<Scalar> uv1, uv2;
            eiToUblasVec(v1, uv1);
            eiToUblasVec(v2, uv2);

            //       std::vector<Scalar> gmmV1(cols), gmmV2(cols);
            //       Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
            //       Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;

            BENCH(uv2 = boost::numeric::ublas::prod(m1, uv1);)
            std::cout << "   a * v:\t" << timer.value() << endl;

            //       BENCH( boost::ublas::prod(gmm::transposed(m1), gmmV1, gmmV2); )
            //       std::cout << "   a' * v:\t" << timer.value() << endl;
        }
#endif

// MTL4
#ifndef NOMTL
        {
            std::cout << "MTL4\t" << density * 100 << "%\n";
            MtlSparse m1(rows, cols);
            eiToMtl(sm1, m1);
            mtl::dense_vector<Scalar> mtlV1(cols, 1.0);
            mtl::dense_vector<Scalar> mtlV2(cols, 1.0);

            timer.reset();
            timer.start();
            for ( int k = 0; k < REPEAT; ++k )
                mtlV2 = m1 * mtlV1;
            timer.stop();
            std::cout << "   a * v:\t" << timer.value() << endl;

            timer.reset();
            timer.start();
            for ( int k = 0; k < REPEAT; ++k )
                mtlV2 = trans(m1) * mtlV1;
            timer.stop();
            std::cout << "   a' * v:\t" << timer.value() << endl;
        }
#endif

        std::cout << "\n\n";
    }

    return 0;
}
